Myelodysplastic syndromes (MDS) comprise a heterogeneous group of hematopoietic stem cell malignancies that share a high frequency of recurrent chromosomal aberrations and a complex pathogenesis (List A F, et al., (2004). “Myelodysplastic syndromes.” Hematology Am Soc Hematol Educ Program: 297-317). MDS malignancies include refractory anemia, refractory cytopenia, myelodysplastic syndrome associated with an isolated del(5q) chromosome abnormality, and unclassifiable myelodysplastic syndrome (National Cancer Institute, General Information About Myelodysplastic Syndromes, 2011). MDS occurs predominantly in older patients, typically over 60 years, though patients as young as 2 years have been reported (Tuncer M A, et al., Primary myelodysplastic syndrome in children: the clinical experience in 33 cases. Br J Haematol 82 (2): 347-53, 1992). Pathogenic mechanisms underlying abnormalities of affected hematopoietic stem cells in MDS remain poorly characterized. Senescence associated accumulation of genetic defects is believed to play a key role in the changes in regulation of apoptosis, differentiation and proliferation potential of affected progenitors (Bernasconi P. Molecular pathways in myelodysplastic syndromes and acute myeloid leukemia: relationships and distinctions—a review. Br J Haematol 2008; 142:695-708). MDS transforms into acute myeloid leukemia (AML) in about 30% of patients after various intervals from diagnosis and at variable rates (National Cancer Institute, General Information About Myelodysplastic Syndromes, 2011).
MDS are diagnosed using an internationally-developed risk analysis, International Prognostic Scoring System (IPSS) for MDS, which analyzed a series of independent risk-based prognostic systems that were combined, collated, and globally analyzed (Greenberg P, et al., International scoring system for evaluating prognosis in myelodysplastic syndromes. Blood 89 (6): 2079-88, 1997). The IPSS uses a multivariate analysis to categorize significant predictors for both survival and AML evolution included bone marrow blast percentage, number of peripheral blood cytopenias, and cytogenetic subgroup, which are used to assign a MDS patient score, which stratifies patients into one of four risk groups: low risk, intermediate-1, intermediate-2, and high risk. The time for the development of AML in the risk groups was 9.4 years, 3.3 years, 1.1 years, and 0.2 years, respectively. Median survival for the groups was 5.7 years, 3.5 years, 1.2 years, and 0.4 years, respectively. MDS is diagnosed in approximately 10,000 people in the United States yearly (Ma X, et al., Myelodysplastic syndromes: incidence and survival in the United States. Cancer 109 (8): 1536-42, 2007).
MicroRNAs (miRNAs) are naturally-occurring, short non-coding RNAs containing 19-25 nucleotides that impair translation or induce mRNA degradation of target mRNA (Fire A, et al., Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 1998; 391:806-811; Bartel D P., MicroRNAs: Genomics, Biogenesis, Mechanism, and Function. Cell. 2004; 116:281-297). The miRNAs are typically processed from 60- to 70-nucleotide fold-back RNA precursors. Over 500 miRNAs were identified in the human genome (Bentwich I, et al., Identification of hundreds of conserved and nonconserved human microRNAs. Nat Genet 2005; 37:766-770). Analysis of miRNA expression in hematologic malignancies and solid tumors has identified expression patterns that are tumor-type specific with prognostic relevance (Calin G A, et al., Frequent deletions and down-regulation of micro-RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci USA. 2002; 99:15524-15529. Epub 2002 Nov. 14; Garzon R, Crose C M. MicroRNAs in normal and malignant hematopoiesis. Curr Opin Hematol 2008; 15: 352-358; Jongen-Lavrencic M, et al., MicroRNA expression profiling in relation to the genetic heterogeneity of acute myeloid leukemia. Blood. 2008 May 15; 111(10):5078-85. Epub 2008 Mar. 12; Debernardi S, et al., MicroRNA miR-181a correlates with morphological sub-class of acute myeloid leukaemia and the expression of its target genes in global genome-wide analysis. Leukemia. 2007; 21:912-916; Garzon R, et al., MicroRNA signatures associated with cytogenetics and prognosis in acute myeloid leukemia. Blood. 2008 Mar. 15; 111(6):3183-9. Epub 2008 Jan. 10; Marcucci G, et al., Prognostic significance of, and gene and microRNA expression signatures associated with, CEBPA mutations in cytogenetically normal acute myeloid leukemia with high-risk molecular features: a Cancer and Leukemia Group B Study. J Clin Oncol. 2008 Nov. 1; 26(31):5078-8). miRNA expression profiling has shown that myeloblasts from patients with acute myeloid leukemia (AML) display an expression profile that is distinct from normal bone marrow progenitors and acute lymphoblastic leukemia (ALL) (Mi S, et al., MicroRNA expression signatures accurately discriminate acute lymphoblastic leukemia from acute myeloid leukemia. Proc Natl Acad Sci USA 2007; 104: 19971-19976).
Marcucci and colleagues identified a microRNA signature with independent predictive power for event-free survival in cytogenetically normal AML, which included five members of the miR-181 family that target genes involved in erythroid differentiation, homeobox genes and toll-like receptors (Marcucci G, et al., Prognostic significance of, and gene and microRNA expression signatures associated with, CEBPA mutations in cytogenetically normal acute myeloid leukemia with high-risk molecular features: a Cancer and Leukemia Group B Study. J Clin Oncol. 2008 Nov. 1; 26(31):5078-87; Marcucci G, et al., MicroRNA expression in cytogenetically normal acute myeloid leukemia. N Engl J. Med. 2008 May 1; 358:1919-28).
MDS diagnosis requires that MDS be differentiated from other diseases, such as anemia, thromocytopenia, and leukopenia. Therefore, the diagnosis may use blood counts, tests on blood cells to eliminate non-MDS cytopenias, bone marrow examination, and genetic analysis/karyotyping of bone marrow aspirate. Moore, et al. (U.S. application Ser. No. 11/667,406) discloses methods of comparing the predictive parameters in a blood sample to a control and assigning a numerical score to the values to indicate when a patient suffers from MDS. Moore, et al. looked at surface markers, such as CD66, CD11a, CD10, CD 116, and CD45. Ma (U.S. Pat. No. 7,790,407) analyzed isoforms of SALL4 to determine the likelihood of MDS. However, methods of reliably and accurately diagnosing patients for MDS is still underdeveloped. Accordingly, a method of using miRNA expressions to diagnose and/or prognose the likelihood of MDS is highly desired.